3 Books on R that All Aspiring Data Scientists Should Read
Earlier this year R was overtaken by Python as the most used programming language for Data Science.
I guess that R is dead then.
R is still a superb language for Data Science, and while it may not be as easy to learn as Python or as quick, if you choose R you can enjoy these benefits:
- Loads of Third-Party packages
- Unmatched graphics and charting capabilities
- Open Source and community development
- Available support
If you're not sure how to get started with R, the 3 books in this blog post will help you make your first steps.
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You can find further details in our TCs.
In this post - the 5th in a series of 8 in which we bring you 21 Inspirational Books for All Aspiring Data Scientists, we highlight 3 books to introduce you to the R programming language and how it is being used in Data Science:
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Practical Data Science with R
- R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics
They are all highly recommended reading and will get your data handling skills in R off the ground in no time...
by Hadley Wickham and Garrett Grolemund
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.
You’ll learn how to:
- Wrangle – transform your datasets into a form convenient for analysis
- Program – learn powerful R tools for solving data problems with greater clarity and ease
- Explore – examine your data, generate hypotheses, and quickly test them
- Model – provide a low-dimensional summary that captures true “signals” in your dataset
- Communicate – learn R Markdown for integrating prose, code, and results
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by Nina Zumel and John Mount
Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you’ll face as you collect, curate, and analyze the data crucial to the success of your business. You’ll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels.
- Data science for the business professional
- Statistical analysis using the R language
- Project lifecycle, from planning to delivery
- Numerous instantly familiar use cases
- Keys to effective data presentations
by Paul Teetor
With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.
Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process.
- Create vectors, handle variables, and perform other basic functions
- Input and output data
- Tackle data structures such as matrices, lists, factors, and data frames
- Work with probability, probability distributions, and random variables
- Calculate statistics and confidence intervals, and perform statistical tests
- Create a variety of graphic displays
- Build statistical models with linear regressions and analysis of variance (ANOVA)
- Explore advanced statistical techniques, such as finding clusters in your data
All 8 posts in the series:
- 21 Inspirational Books for All Aspiring Data Scientists:
- 3 Great Data Science Books for Aspiring Data Scientists
- 3 Must-Read Statistics Books for Aspiring Data Scientists
- 3 Essential Python Books for Aspiring Data Scientists
- 3 Books on R That all Aspiring Data Scientists Should Read
- 3 Inspirational Machine Learning Books for Aspiring Data Scientists
- 3 Essential Visualisation Books for Aspiring Data Scientists
- 3 Must-Read Books on Data Ethics for Aspiring Data Scientists
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